Arguments of Nominals in Semantic Interpretation of Biomedical Text

Based on linguistic generalizations, we enhanced an existing semantic processor, SemRep, for effective interpretation of a wide range of patterns used to express arguments of nominalization in clinically oriented biomedical text. Nominalizations are pervasive in the scientific literature, yet few text mining systems adequately address them, thus missing a wealth of information. We evaluated the system by assessing the algorithm independently and by determining its contribution to SemRep generally. The first evaluation demonstrated the strength of the method through an F-score of 0.646 (P=0.743, R=0.569), which is more than 20% higher than the baseline. The second evaluation showed that overall SemRep results were increased to F-score 0.689 (P=0.745, R=0.640), approximately 25% better than processing without nominalizations.